Closed-loop Feedback Mechanism Research Articles

Software cybernetics in BPM: Modeling software behavior as feedback for evolution by a novel discovery method based on augmented event logs

Interpret business process management (BPM) with the view of cybernetics.Discuss the relation between software cybernetics and BPM.Analyze the role of process discovery in software cybernetics in context of BPM.Propose a novel process discovery method based on a kind of augmented event logs.All implicit places and implicit dependencies can be found. Business Process Management (BPM) is a quickly developing management theory in recent years. The goal of BPM is to improve corporate performance by managing and optimizing the businesses process in and among enterprises. The goal is easier to achieve with the closed-loop feedback mechanism from business process execution to redesign in BPM life cycle, where the business process itself and the set of activities in BPM are viewed as a controlled object and a controller respectively. In this feedback control system, process mining plays an important role in generating feedback of process execution for redesign. However, the existing discovery methods cannot mine certain special structures from execution logs (e.g., implicit dependency, implicit place and short loops) correctly and their mining efficiencies cannot meet the requirements of online process mining. In this paper, we propose a novel discovery method to overcome these challenges based on a kind of augmented event log that will also bring new research directions for process discovery. A case study is presented for introducing how the mined model can be used in business process evolution. Results of experiments are described to show the improvements of the proposed algorithm compared with others.

Using computational intelligence to forecast carbon prices

European Union has introduced the European Trading System (ETS) as a tool for developing and implementing international treaties related to climate changes and to identify the most cost-effective methods for reducing greenhouse gas emissions, in particular carbon dioxide (CO2), which is the most substantial. Companies producing carbon emissions must effectively manage associated costs by buying or selling carbon emission futures. Viewed from this perspective, this paper provides a model for managing the risk by buying and selling carbon emission futures by implementing techniques that leverage computational intelligence. Three computational intelligence techniques are proposed to provide accurate and timely forecasts for changes in the price of carbon: a novel hybrid neuro-fuzzy controller that forms a closed-loop feedback mechanism called PATSOS; an artificial neural network (ANN) based system; an adaptive neuro-fuzzy inference system (ANFIS). Results are based on 1074 daily carbon price observations collected to comprise a useful time-series dataset and for evaluation of the proposed techniques. The extra-sample performance of the proposed techniques is calculated. Analysis results are compared with those produced by other models. Comparison studies reveal that PATSOS is the most accurate and promising methodology for predicting the price of carbon. It is stated that this paper registers a first attempt to apply a hybrid neuro-fuzzy controller to forecasting carbon prices.

Closed-Loop Feedback Computation Model of Dynamical Reputation Based on the Local Trust Evaluation in Business-to-Consumer E-Commerce

Trust and reputation are important factors that influence the success of both traditional transactions in physical social networks and modern e-commerce in virtual Internet environments. It is difficult to define the concept of trust and quantify it because trust has both subjective and objective characteristics at the same time. A well-reported issue with reputation management system in business-to-consumer (BtoC) e-commerce is the “all good reputation” problem. In order to deal with the confusion, a new computational model of reputation is proposed in this paper. The ratings of each customer are set as basic trust score events. In addition, the time series of massive ratings are aggregated to formulate the sellers’ local temporal trust scores by Beta distribution. A logical model of trust and reputation is established based on the analysis of the dynamical relationship between trust and reputation. As for single goods with repeat transactions, an iterative mathematical model of trust and reputation is established with a closed-loop feedback mechanism. Numerical experiments on repeated transactions recorded over a period of 24 months are performed. The experimental results show that the proposed method plays guiding roles for both theoretical research into trust and reputation and the practical design of reputation systems in BtoC e-commerce.

Dynamical Trust and Reputation Computation Model for B2C E-Commerce

Trust is one of the most important factors that influence the successful application of network service environments, such as e-commerce, wireless sensor networks, and online social networks. Computation models associated with trust and reputation have been paid special attention in both computer societies and service science in recent years. In this paper, a dynamical computation model of reputation for B2C e-commerce is proposed. Firstly, conceptions associated with trust and reputation are introduced, and the mathematical formula of trust for B2C e-commerce is given. Then a dynamical computation model of reputation is further proposed based on the conception of trust and the relationship between trust and reputation. In the proposed model, classical varying processes of reputation of B2C e-commerce are discussed. Furthermore, the iterative trust and reputation computation models are formulated via a set of difference equations based on the closed-loop feedback mechanism. Finally, a group of numerical simulation experiments are performed to illustrate the proposed model of trust and reputation. Experimental results show that the proposed model is effective in simulating the dynamical processes of trust and reputation for B2C e-commerce.

Altered characteristics of balance control in obese older adults

Obesity is one of the most significant epidemiological trends of the last decades. Recently it was found that obese individuals show postural instability. Balance control mechanisms in obese older adults were less studied. Therefore we aimed to investigate the effect of obesity on balance control mechanisms in older adults. Parameters from Stabilogram-Diffusion Analysis (SDA) and measures from summary statistics of foot centre-of-pressure (COP) displacements along the anterior-posterior (AP) and mediolateral (ML) directions in eyes open and eyes closed conditions were used to characterize postural control in 22 obese (30-<35kg/m(2)), 26 overweight (25-<30kg/m(2)), and 18 normal weight subjects (18.5-<25kg/m(2)). Obese group subjects demonstrated significantly greater transition displacement, transition time interval, and short-term scaling exponent in the ML-direction compared with the normal weight group (eyes open and closed). In the AP-direction the obese group showed greater transition displacement (eyes open) and short-term scaling exponent (eyes open and closed). Average AP-COP and ML-COP ranges of COP sway were higher in the obese group compared with the normal weight group (eyes open and closed). This work indicates an altered postural control process in obese older adults. A greater sway displacement before closed-loop feedback mechanisms are called into play was seen in the ML direction that may lead to a higher risk of instability and fall events.

Non-Gaussian correlated process sampling for Bayesian cognitive target classification

A cognitive signal processing system (for example, in radar or sonar) is one that observes and learns from the environment; then uses a dynamic closed-loop feedback mechanism to adapt the illumination waveform so as to provide system performance improvements over traditional systems. Current cognitive radar algorithms are designed only for target impulse responses that are Gaussian distributed to achieve mathematical tractability. Our research generalizes the cognitive radar target classifier to deal effectively with arbitrary non-Gaussian distributed target responses. Given exemplars of target impulse responses, our Bayesian illumination waveform design algorithm requires the ability to draw complex correlated samples from a target distribution specified by both an arbitrary desired probability density function and a desired power spectral density. This capability is realized using kernel density estimation and an extension of a new simple and efficient nonlinear sampling algorithm by Nichols et al. Simulations using non-Gaussian target impulse response waveforms demonstrate very effective target classification performance. We discuss practical issues with the application of the algorithms to real-world problems.

Statistical sampling and Bayesian illumination waveform design for multiple-hypothesis target classification in cognitive signal processing

Statistical sampling algorithms are widely used in Bayesian signal processing for drawing real-valued independent, identically distributed (i.i.d.) samples from a desired distribution. This paper focuses on the more difficult problem of how to draw complex correlated samples from a distribution specified by both an arbitrary desired probability density function and a desired power spectral density. This problem arises in cognitive signal processing. A cognitive signal processing system (for example, in radar or sonar) is one that observes and learns from the environment; then uses a dynamic closed-loop feedback mechanism to adapt the illumination waveform so as to provide system performance improvements over traditional systems. Current cognitive radar algorithms focus only on target impulse responses that are Gaussian distributed to achieve mathematical tractability. This research generalizes the cognitive radar target classifier to deal effectively with arbitrary non-Gaussian distributed target responses. The key contribution lies in the use of a kernel density estimator and an extension of a new algorithm by Nichols et al. for drawing complex correlated samples from target distributions specified by both an arbitrary desired probability density function and a desired power spectral density. Simulations using non-Gaussian target impulse response waveforms demonstrate very effective classification performance.

Analysis of human vergence dynamics

Disparity vergence is commonly viewed as being controlled by at least two mechanisms, an open-loop vergence-specific burst mechanism analogous to the ballistic drive of saccades, and a closed-loop feedback mechanism controlled by the disparity error. We show that human vergence dynamics for disparity jumps of a large textured field have a typical time course consistent with predominant control by the open-loop vergence-specific burst mechanism, although various subgroups of the population show radically different vergence behaviors. Some individuals show markedly slow divergence responses, others slow convergence responses, others slow responses in both vergence directions, implying that the two vergence directions have separate control mechanisms. The faster time courses usually had time-symmetric velocity waveforms implying open-loop burst control, while the slow response waveforms were usually time-asymmetric implying closed-loop feedback control. A further type of behavior seen in a distinct subpopulation was a compound anomalous divergence response consisting of an initial convergence movement followed by a large corrective divergence movement with time courses implying closed-loop feedback control. This analysis of the variety of human vergence responses thus contributes substantially to the understanding of the oculomotor control mechanisms underlying the generation of vergence movements [corrected].

Illumination waveform design for non-Gaussian multi-hypothesis target classification in cognitive radar

A cognitive radar (CR) system is one that observes and learns from the environment; then uses a dynamic closed-loop feedback mechanism to adapt the illumination waveform so as to provide system performance improvements over traditional radar systems. Romero et al. recently developed a CR system that performs multiple hypothesis target classification and exploits the spectral sparsity of correlated narrowband target responses to achieve significant performance improvements over traditional radars that use wideband illumination pulses. This CR system was designed for Gaussian target responses. This research generalizes the CR system to deal effectively with arbitrary non-Gaussian distributed target responses via two key contributions: (1) an important statistical expected value operation that is usually evaluated in closed form is evaluated numerically using an ensemble averaging operation, and (2) a powerful new statistical sampling algorithm and a kernel density estimator are applied to draw complex target samples from target distributions specified by both a desired power spectral density and an arbitrary desired probability density function. Simulations using non-Gaussian targets demonstrate very effective algorithm performance.

Influence of Test Machine Control Method on Flexural Performance of Fiber Reinforced Concrete Beams

Abstract Flexural testing of fiber reinforced concrete (FRC) and shotcrete using beams has undergone a steady evolution over the past 30 years to the point where complex servo-controlled test machines are now required to undertake these tests. These machines have been found necessary in order to impose stable and reproducible test conditions on beams. Servo-controlled machines utilize closed-loop strain feedback mechanisms in an attempt to maintain stability throughout the post-crack deformation history of a brittle FRC specimen. However, some standard test methods for flexural performance assessment of FRC require displacement-control of the loading piston rather than strain-control based on the measured deformation of the beam. Displacement-control has frequently been found to result in unstable unloading of brittle FRC. Anecdotal evidence suggests that differences in the method of machine control may lead to changes in the apparent performance of specimens. An investigation was therefore instigated to examine the apparent performance of FRC beams tested using strain-controlled loading compared to displacement-control.

Studying metabolic regulation in human muscle.

Phosphorus magnetic resonance spectroscopy, despite some limitations, is a valuable non-invasive window on muscle metabolism in vivo, particularly oxidative ATP synthesis. A number of experiments have shown this to be dominated by closed-loop feedback mechanisms: a well-known model posits regulation by ADP, but there are others, difficult to distinguish experimentally. Moreover the contribution of open-loop control mechanisms ('feed forward' or 'parallel activation') in vivo remains controversial. Progress will require more precise data, better integrated with other measurements (e.g. muscle oxygenation), and improvement of the conceptual tools appropriate to such studies, where data are limited and steady-state assumptions frequently inapplicable.

Analysis of neural-network-based congestion control algorithms for ATM networks

Congestion control provides a challenge in the design of Asynchronous Transfer Mode (ATM) networks. Several algorithms have been proposed in the literature for alleviating or reducing congestion. In this paper the Jumping Window (JW), Triggered Jumping Window (TJW) and the Exponentially Weighted Moving Average (EWMA) window algorithms are analyzed, based on a closed-loop predictive feedback mechanism using a neural network. Single- and multiple-source models with real-world and simulated data are used to test the performance of the proposed mechanisms. The consequences of delayed feedback messages is also considered. Results indicate that neural controllers are effective in reducing the cell loss rate, while introducing minimal additional delays.

Flow control in ATM networks: a survey

In this paper, we present results of a study of flow control in ATM networks. Two classes of flow control are described: preventive and reactive. A preventive control, which uses a static resources allocation, seems inadequate to handle rough traffic in high speed networks. On the other hand, the reactive control schemes, which employ a closed-loop feedback mechanism, seem more attractive than the preventive control schemes, especially in ATM networks. Four ATM service categories are provided to serve a variety of users. To enable each service class to function effectively two closed-loop flow control mechanisms: rate-based and credit-based, have been introduced for ATM networks. Rate-based flow control schemes use the rate of traffic flowing from the source to control the transmission rate. In this paper, a number of variants of rate-based schemes are described and analysed. Credit-based schemes use window flow control where a traffic source is allowed to transmit data only when there is available buffer space in the downstream node. In this paper, a number of credit-based schemes are also described and analysed.

Novel Ovarian Regulatory Peptides: Inhibin, Activin, and Follistatin

Based on the extensive amount of research on inhibin and related polypeptides accomplished during the past 5 years, the inhibin concept put forth more than 50 years ago has not only become well established but also more complex than originally imagined. The closed-loop feedback mechanism of ovarian inhibin and pituitary FSH has been joined by possible "inhibin-like" actions of follistatin and FSH-stimulatory effects of activin. In addition, in vitro experiments suggest possible autocrine and paracrine functions for the gonadal polypeptide hormones. Figure 3 shows a simplistic diagram summarizing our current understanding of inhibin/activin and follistatin action along the hypothalamic-pituitary-gonadal axis. Hopefully, research in the coming years will allow us to remove the many question marks still remaining but will undoubtedly add others.